Overview

Dataset statistics

Number of variables21
Number of observations891
Missing cells687
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory561.2 KiB
Average record size in memory645.0 B

Variable types

Numeric7
Categorical11
Text3

Alerts

DatasetName has constant value ""Constant
Title is highly imbalanced (53.4%)Imbalance
Cabin has 687 (77.1%) missing valuesMissing
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros

Reproduction

Analysis started2024-03-18 16:53:19.122780
Analysis finished2024-03-18 16:53:25.927624
Duration6.8 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:26.019378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2024-03-18T13:53:26.204883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.3 KiB
0.0
549 
1.0
342 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2673
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 549
61.6%
1.0 342
38.4%

Length

2024-03-18T13:53:26.351490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:26.466184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 549
61.6%
1.0 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1782
66.7%
Other Punctuation 891
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1440
80.8%
1 342
 
19.2%
Other Punctuation
ValueCountFrequency (%)
. 891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2673
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1440
53.9%
. 891
33.3%
1 342
 
12.8%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2024-03-18T13:53:26.587828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:26.708536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size73.2 KiB
2024-03-18T13:53:26.918942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2024-03-18T13:53:27.407667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15446
64.3%
Uppercase Letter 3645
 
15.2%
Space Separator 2735
 
11.4%
Other Punctuation 1899
 
7.9%
Close Punctuation 144
 
0.6%
Open Punctuation 144
 
0.6%
Dash Punctuation 13
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1958
12.7%
e 1703
11.0%
a 1657
10.7%
i 1325
8.6%
n 1304
8.4%
s 1297
8.4%
l 1067
 
6.9%
o 1008
 
6.5%
t 667
 
4.3%
h 517
 
3.3%
Other values (16) 2943
19.1%
Uppercase Letter
ValueCountFrequency (%)
M 1128
30.9%
A 250
 
6.9%
J 215
 
5.9%
H 203
 
5.6%
S 180
 
4.9%
C 172
 
4.7%
E 166
 
4.6%
W 143
 
3.9%
B 140
 
3.8%
L 129
 
3.5%
Other values (15) 919
25.2%
Other Punctuation
ValueCountFrequency (%)
. 892
47.0%
, 891
46.9%
" 106
 
5.6%
' 9
 
0.5%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2735
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19091
79.5%
Common 4935
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1958
 
10.3%
e 1703
 
8.9%
a 1657
 
8.7%
i 1325
 
6.9%
n 1304
 
6.8%
s 1297
 
6.8%
M 1128
 
5.9%
l 1067
 
5.6%
o 1008
 
5.3%
t 667
 
3.5%
Other values (41) 5977
31.3%
Common
ValueCountFrequency (%)
2735
55.4%
. 892
 
18.1%
, 891
 
18.1%
) 144
 
2.9%
( 144
 
2.9%
" 106
 
2.1%
- 13
 
0.3%
' 9
 
0.2%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2024-03-18T13:53:27.584193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:27.717806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4192
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4192
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

Distinct89
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.202211
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:27.858464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q121
median26
Q336.75
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.549459
Coefficient of variation (CV)0.46398744
Kurtosis0.518195
Mean29.202211
Median Absolute Deviation (MAD)8
Skewness0.47353369
Sum26019.17
Variance183.58785
MonotonicityNot monotonic
2024-03-18T13:53:28.033997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 108
 
12.1%
18 59
 
6.6%
30 35
 
3.9%
24 30
 
3.4%
22 27
 
3.0%
31 26
 
2.9%
19 25
 
2.8%
28 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
Other values (79) 509
57.1%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:28.193586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2024-03-18T13:53:28.321246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:28.441962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2024-03-18T13:53:28.562643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size55.6 KiB
2024-03-18T13:53:28.785045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%
2024-03-18T13:53:29.192958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4808
79.9%
Uppercase Letter 652
 
10.8%
Other Punctuation 295
 
4.9%
Space Separator 239
 
4.0%
Lowercase Letter 21
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 151
23.2%
O 100
15.3%
P 98
15.0%
A 82
12.6%
S 74
11.3%
N 40
 
6.1%
T 36
 
5.5%
W 16
 
2.5%
Q 15
 
2.3%
I 11
 
1.7%
Other values (6) 29
 
4.4%
Decimal Number
ValueCountFrequency (%)
3 746
15.5%
1 689
14.3%
2 594
12.4%
7 490
10.2%
4 464
9.7%
6 422
8.8%
0 406
8.4%
5 387
8.0%
9 328
6.8%
8 282
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
a 6
28.6%
s 5
23.8%
r 4
19.0%
i 4
19.0%
l 1
 
4.8%
e 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 197
66.8%
/ 98
33.2%
Space Separator
ValueCountFrequency (%)
239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5342
88.8%
Latin 673
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 151
22.4%
O 100
14.9%
P 98
14.6%
A 82
12.2%
S 74
11.0%
N 40
 
5.9%
T 36
 
5.3%
W 16
 
2.4%
Q 15
 
2.2%
I 11
 
1.6%
Other values (12) 50
 
7.4%
Common
ValueCountFrequency (%)
3 746
14.0%
1 689
12.9%
2 594
11.1%
7 490
9.2%
4 464
8.7%
6 422
7.9%
0 406
7.6%
5 387
7.2%
9 328
6.1%
8 282
 
5.3%
Other values (3) 534
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

Distinct247
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.686134
Minimum4.0125
Maximum512.3292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:29.375467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.0125
5-th percentile7.2292
Q17.925
median14.5
Q331.275
95-th percentile112.07915
Maximum512.3292
Range508.3167
Interquartile range (IQR)23.35

Descriptive statistics

Standard deviation49.609604
Coefficient of variation (CV)1.5177569
Kurtosis33.455421
Mean32.686134
Median Absolute Deviation (MAD)6.7708
Skewness4.7873819
Sum29123.345
Variance2461.1128
MonotonicityNot monotonic
2024-03-18T13:53:29.540010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 47
 
5.3%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
26.55 15
 
1.7%
Other values (237) 611
68.6%
ValueCountFrequency (%)
4.0125 1
0.1%
5 1
0.1%
6.2375 1
0.1%
6.4375 1
0.1%
6.45 1
0.1%
6.4958 2
0.2%
6.75 2
0.2%
6.8583 1
0.1%
6.95 1
0.1%
6.975 2
0.2%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Text

MISSING 

Distinct147
Distinct (%)72.1%
Missing687
Missing (%)77.1%
Memory size33.7 KiB
2024-03-18T13:53:29.788367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.5882353
Min length1

Characters and Unicode

Total characters732
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)49.5%

Sample

1st rowC85
2nd rowC123
3rd rowE46
4th rowG6
5th rowC103
ValueCountFrequency (%)
c23 4
 
1.7%
c27 4
 
1.7%
g6 4
 
1.7%
b96 4
 
1.7%
b98 4
 
1.7%
f 4
 
1.7%
c25 4
 
1.7%
f33 3
 
1.3%
e101 3
 
1.3%
f2 3
 
1.3%
Other values (151) 201
84.5%
2024-03-18T13:53:30.221178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 460
62.8%
Uppercase Letter 238
32.5%
Space Separator 34
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 72
15.7%
1 61
13.3%
3 59
12.8%
6 51
11.1%
5 45
9.8%
4 37
8.0%
8 37
8.0%
7 34
7.4%
9 33
7.2%
0 31
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494
67.5%
Latin 238
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 72
14.6%
1 61
12.3%
3 59
11.9%
6 51
10.3%
5 45
9.1%
4 37
7.5%
8 37
7.5%
34
6.9%
7 34
6.9%
9 33
6.7%
Latin
ValueCountFrequency (%)
C 71
29.8%
B 64
26.9%
D 34
14.3%
E 33
13.9%
A 15
 
6.3%
F 13
 
5.5%
G 7
 
2.9%
T 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 72
 
9.8%
C 71
 
9.7%
B 64
 
8.7%
1 61
 
8.3%
3 59
 
8.1%
6 51
 
7.0%
5 45
 
6.1%
4 37
 
5.1%
8 37
 
5.1%
34
 
4.6%
Other values (9) 201
27.5%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
S
646 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Length

2024-03-18T13:53:30.389758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:30.506447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
s 646
72.5%
c 168
 
18.9%
q 77
 
8.6%

Most occurring characters

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 891
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 891
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

DatasetName
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
train
891 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4455
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrain
2nd rowtrain
3rd rowtrain
4th rowtrain
5th rowtrain

Common Values

ValueCountFrequency (%)
train 891
100.0%

Length

2024-03-18T13:53:30.722868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:30.834570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
train 891
100.0%

Most occurring characters

ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4455
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4455
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 891
20.0%
r 891
20.0%
a 891
20.0%
i 891
20.0%
n 891
20.0%

Title
Categorical

IMBALANCE 

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size52.1 KiB
Mr
517 
Miss
185 
Mrs
127 
Master
 
40
Dr
 
7
Other values (8)
 
15

Length

Max length8
Median length2
Mean length2.7721661
Min length2

Characters and Unicode

Total characters2470
Distinct characters24
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 517
58.0%
Miss 185
 
20.8%
Mrs 127
 
14.3%
Master 40
 
4.5%
Dr 7
 
0.8%
Rev 6
 
0.7%
Major 2
 
0.2%
Col 2
 
0.2%
Lady 1
 
0.1%
Sir 1
 
0.1%
Other values (3) 3
 
0.3%

Length

2024-03-18T13:53:30.962229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 517
58.0%
miss 185
 
20.8%
mrs 127
 
14.3%
master 40
 
4.5%
dr 7
 
0.8%
rev 6
 
0.7%
major 2
 
0.2%
col 2
 
0.2%
lady 1
 
0.1%
sir 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
M 871
35.3%
r 695
28.1%
s 539
21.8%
i 186
 
7.5%
e 49
 
2.0%
a 44
 
1.8%
t 42
 
1.7%
D 7
 
0.3%
o 6
 
0.2%
R 6
 
0.2%
Other values (14) 25
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1579
63.9%
Uppercase Letter 891
36.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 695
44.0%
s 539
34.1%
i 186
 
11.8%
e 49
 
3.1%
a 44
 
2.8%
t 42
 
2.7%
o 6
 
0.4%
v 6
 
0.4%
j 2
 
0.1%
l 2
 
0.1%
Other values (7) 8
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
M 871
97.8%
D 7
 
0.8%
R 6
 
0.7%
C 4
 
0.4%
L 1
 
0.1%
S 1
 
0.1%
J 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 871
35.3%
r 695
28.1%
s 539
21.8%
i 186
 
7.5%
e 49
 
2.0%
a 44
 
1.8%
t 42
 
1.7%
D 7
 
0.3%
o 6
 
0.2%
R 6
 
0.2%
Other values (14) 25
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 871
35.3%
r 695
28.1%
s 539
21.8%
i 186
 
7.5%
e 49
 
2.0%
a 44
 
1.8%
t 42
 
1.7%
D 7
 
0.3%
o 6
 
0.2%
R 6
 
0.2%
Other values (14) 25
 
1.0%

FamilySize
Real number (ℝ)

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:31.083904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2024-03-18T13:53:31.221539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%

IsAlone
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
1
537 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Length

2024-03-18T13:53:31.360134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:31.474859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring characters

ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

AgeGroup
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Adult
523 
MiddleAge
166 
Teenager
104 
Child
72 
Senior
 
26

Length

Max length9
Median length5
Mean length6.1245791
Min length5

Characters and Unicode

Total characters5457
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult
2nd rowAdult
3rd rowAdult
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 523
58.7%
MiddleAge 166
 
18.6%
Teenager 104
 
11.7%
Child 72
 
8.1%
Senior 26
 
2.9%

Length

2024-03-18T13:53:31.615461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:31.766049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
adult 523
58.7%
middleage 166
 
18.6%
teenager 104
 
11.7%
child 72
 
8.1%
senior 26
 
2.9%

Most occurring characters

ValueCountFrequency (%)
d 927
17.0%
l 761
13.9%
A 689
12.6%
e 670
12.3%
u 523
9.6%
t 523
9.6%
g 270
 
4.9%
i 264
 
4.8%
M 166
 
3.0%
n 130
 
2.4%
Other values (7) 534
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4400
80.6%
Uppercase Letter 1057
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 927
21.1%
l 761
17.3%
e 670
15.2%
u 523
11.9%
t 523
11.9%
g 270
 
6.1%
i 264
 
6.0%
n 130
 
3.0%
r 130
 
3.0%
a 104
 
2.4%
Other values (2) 98
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 689
65.2%
M 166
 
15.7%
T 104
 
9.8%
C 72
 
6.8%
S 26
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5457
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 927
17.0%
l 761
13.9%
A 689
12.6%
e 670
12.3%
u 523
9.6%
t 523
9.6%
g 270
 
4.9%
i 264
 
4.8%
M 166
 
3.0%
n 130
 
2.4%
Other values (7) 534
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 927
17.0%
l 761
13.9%
A 689
12.6%
e 670
12.3%
u 523
9.6%
t 523
9.6%
g 270
 
4.9%
i 264
 
4.8%
M 166
 
3.0%
n 130
 
2.4%
Other values (7) 534
9.8%

FarePerPerson
Real number (ℝ)

Distinct289
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.398301
Minimum1.1321429
Maximum512.3292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-03-18T13:53:31.942608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.1321429
5-th percentile4.5419643
Q17.5896
median8.6625
Q324.5
95-th percentile64.1021
Maximum512.3292
Range511.19706
Interquartile range (IQR)16.9104

Descriptive statistics

Standard deviation35.890509
Coefficient of variation (CV)1.7594852
Kurtosis86.397483
Mean20.398301
Median Absolute Deviation (MAD)3.04165
Skewness7.7046813
Sum18174.886
Variance1288.1286
MonotonicityNot monotonic
2024-03-18T13:53:32.113145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 59
 
6.6%
8.05 54
 
6.1%
7.75 39
 
4.4%
7.8958 38
 
4.3%
10.5 28
 
3.1%
26.55 23
 
2.6%
7.925 16
 
1.8%
7.775 15
 
1.7%
26 14
 
1.6%
7.2292 13
 
1.5%
Other values (279) 592
66.4%
ValueCountFrequency (%)
1.132142857 1
0.1%
2.409733333 2
0.2%
2.583333333 1
0.1%
2.618066667 1
0.1%
2.641666667 2
0.2%
2.875 1
0.1%
2.8875 1
0.1%
3.125 1
0.1%
3.2479 1
0.1%
3.4875 1
0.1%
ValueCountFrequency (%)
512.3292 2
0.2%
256.1646 1
 
0.1%
227.525 3
0.3%
221.7792 1
 
0.1%
211.3375 1
 
0.1%
153.4625 1
 
0.1%
151.55 1
 
0.1%
146.5208 1
 
0.1%
135.6333 3
0.3%
134.5 1
 
0.1%

FareQuartile
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
2
229 
0
226 
1
220 
3
216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%

Length

2024-03-18T13:53:32.286657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:32.416342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%

Most occurring characters

ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 229
25.7%
0 226
25.4%
1 220
24.7%
3 216
24.2%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
0
228 
1
223 
2
222 
3
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

Length

2024-03-18T13:53:32.552976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:32.686618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

Most occurring characters

ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
25.6%
1 223
25.0%
2 222
24.9%
3 218
24.5%

CabinPrefix
Categorical

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
F
432 
E
159 
G
105 
C
99 
B
47 
Other values (3)
49 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowF
2nd rowC
3rd rowF
4th rowC
5th rowE

Common Values

ValueCountFrequency (%)
F 432
48.5%
E 159
 
17.8%
G 105
 
11.8%
C 99
 
11.1%
B 47
 
5.3%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%

Length

2024-03-18T13:53:32.835191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:53:32.973849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 432
48.5%
e 159
 
17.8%
g 105
 
11.8%
c 99
 
11.1%
b 47
 
5.3%
d 33
 
3.7%
a 15
 
1.7%
t 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 432
48.5%
E 159
 
17.8%
G 105
 
11.8%
C 99
 
11.1%
B 47
 
5.3%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 891
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 432
48.5%
E 159
 
17.8%
G 105
 
11.8%
C 99
 
11.1%
B 47
 
5.3%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 891
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 432
48.5%
E 159
 
17.8%
G 105
 
11.8%
C 99
 
11.1%
B 47
 
5.3%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 432
48.5%
E 159
 
17.8%
G 105
 
11.8%
C 99
 
11.1%
B 47
 
5.3%
D 33
 
3.7%
A 15
 
1.7%
T 1
 
0.1%

Interactions

2024-03-18T13:53:24.521382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:19.536675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.421341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.266083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.101819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.888714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.724511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.629096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:19.727196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.533045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.374792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.207534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.018368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.825242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.842525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:19.846876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.656712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.502451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.328245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.152012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.945927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.962206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:19.985508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.781381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.626122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.445898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.281665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.081555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:25.095846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.103160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.915022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.741812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.558628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.396357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.197217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:25.204556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.212866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.030715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.855475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.667337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.505067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.305958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:25.314239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:20.315621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.147400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:21.981171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:22.774053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:23.615805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-18T13:53:24.411677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-03-18T13:53:25.493780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:53:25.809940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleFamilySizeIsAloneAgeGroupFarePerPersonFareQuartileFarePerPersonQuartileCabinPrefix
010.03Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNStrainMr20Adult3.6250000F
121.01Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85CtrainMrs20Adult35.6416533C
231.03Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNStrainMiss11Adult7.9250001F
341.01Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123StrainMrs20Adult26.5500033C
450.03Allen, Mr. William Henrymale35.0003734508.0500NaNStrainMr11Adult8.0500011E
560.03Moran, Mr. Jamesmale26.0003308778.4583NaNQtrainMr11Adult8.4583011E
670.01McCarthy, Mr. Timothy Jmale54.0001746351.8625E46StrainMr11MiddleAge51.8625033E
780.03Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNStrainMaster50Child4.2150020G
891.03Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNStrainMrs30Adult3.7111010E
9101.02Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNCtrainMrs20Teenager15.0354022F
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleFamilySizeIsAloneAgeGroupFarePerPersonFareQuartileFarePerPersonQuartileCabinPrefix
8818820.03Markun, Mr. Johannmale33.0003492577.8958NaNStrainMr11Adult7.89580001F
8828830.03Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNStrainMiss11Adult10.51670012E
8838840.02Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNStrainMr11Adult10.50000012F
8848850.03Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNStrainMr11Adult7.05000000F
8858860.03Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQtrainMrs60Adult4.85416720G
8868870.02Montvila, Rev. Juozasmale27.00021153613.0000NaNStrainRev11Adult13.00000012F
8878881.01Graham, Miss. Margaret Edithfemale19.00011205330.0000B42StrainMiss11Adult30.00000023B
8888890.03Johnston, Miss. Catherine Helen "Carrie"female18.012W./C. 660723.4500NaNStrainMiss40Teenager5.86250020G
8898901.01Behr, Mr. Karl Howellmale26.00011136930.0000C148CtrainMr11Adult30.00000023C
8908910.03Dooley, Mr. Patrickmale32.0003703767.7500NaNQtrainMr11Adult7.75000001F